Overview

Dataset statistics

Number of variables30
Number of observations34114
Missing cells227366
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 MiB
Average record size in memory240.0 B

Variable types

Numeric20
Categorical10

Alerts

SUBJECT_ID is highly overall correlated with GROUP_IDHigh correlation
DISEASE_DURATION is highly overall correlated with REL_DAYHigh correlation
Height is highly overall correlated with GENDER_F and 1 other fieldsHigh correlation
REL_DAY is highly overall correlated with DISEASE_DURATIONHigh correlation
Blood_Pressure_Manual_Systolic is highly overall correlated with AGE and 3 other fieldsHigh correlation
Blood_Pressure_Manual_Diastolic is highly overall correlated with Blood_Pressure_Manual_Systolic and 1 other fieldsHigh correlation
Arterial_Blood_Pressure_Systolic is highly overall correlated with Blood_Pressure_Manual_Systolic and 1 other fieldsHigh correlation
Arterial_Blood_Pressure_Diastolic is highly overall correlated with Arterial_Blood_Pressure_MeanHigh correlation
Arterial_Blood_Pressure_Mean is highly overall correlated with Blood_Pressure_Manual_Systolic and 2 other fieldsHigh correlation
GROUP_ID is highly overall correlated with SUBJECT_IDHigh correlation
GENDER_F is highly overall correlated with Height and 1 other fieldsHigh correlation
GENDER_M is highly overall correlated with Height and 1 other fieldsHigh correlation
MARITAL_STATUS_LIFE PARTNER is highly overall correlated with Blood_Pressure_Manual_Systolic and 1 other fieldsHigh correlation
MARITAL_STATUS_MARRIED is highly overall correlated with MARITAL_STATUS_SINGLE and 1 other fieldsHigh correlation
MARITAL_STATUS_SINGLE is highly overall correlated with MARITAL_STATUS_MARRIEDHigh correlation
AGE is highly overall correlated with Blood_Pressure_Manual_SystolicHigh correlation
MARITAL_STATUS_WIDOWED is highly overall correlated with MARITAL_STATUS_MARRIEDHigh correlation
Height has 31373 (92.0%) missing valuesMissing
Daily Weight has 21696 (63.6%) missing valuesMissing
Heart Rate Alarm - Low has 13791 (40.4%) missing valuesMissing
Heart rate Alarm - High has 13791 (40.4%) missing valuesMissing
Oxygen_Saturation_Alarm_High has 3042 (8.9%) missing valuesMissing
Oxygen_Saturation_Alarm_Low has 3008 (8.8%) missing valuesMissing
Oxygen_Pressure has 22285 (65.3%) missing valuesMissing
Blood_Pressure_Manual_Systolic has 33831 (99.2%) missing valuesMissing
Blood_Pressure_Manual_Diastolic has 33849 (99.2%) missing valuesMissing
Arterial_Blood_Pressure_Systolic has 16857 (49.4%) missing valuesMissing
Arterial_Blood_Pressure_Diastolic has 16856 (49.4%) missing valuesMissing
Arterial_Blood_Pressure_Mean has 16788 (49.2%) missing valuesMissing
Daily Weight is highly skewed (γ1 = 111.4360068)Skewed
Heart Rate Alarm - Low is highly skewed (γ1 = 90.12909524)Skewed
Heart rate Alarm - High is highly skewed (γ1 = 136.8670599)Skewed
Oxygen_Saturation is highly skewed (γ1 = 184.3770145)Skewed
Oxygen_Saturation_Alarm_High is highly skewed (γ1 = 150.5764458)Skewed
Oxygen_Saturation_Alarm_Low is highly skewed (γ1 = 175.9549229)Skewed
Arterial_Blood_Pressure_Diastolic is highly skewed (γ1 = 52.32333598)Skewed
REL_DAY has 7097 (20.8%) zerosZeros

Reproduction

Analysis started2023-01-25 08:41:59.731665
Analysis finished2023-01-25 08:43:01.856329
Duration1 minute and 2.12 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

SUBJECT_ID
Real number (ℝ)

Distinct4278
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41697.253
Minimum30
Maximum99995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:01.979301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile3722
Q118208.25
median30678
Q366786
95-th percentile93870
Maximum99995
Range99965
Interquartile range (IQR)48577.75

Descriptive statistics

Standard deviation29057.837
Coefficient of variation (CV)0.69687653
Kurtosis-1.0596829
Mean41697.253
Median Absolute Deviation (MAD)20182
Skewness0.46880994
Sum1.4224601 × 109
Variance8.443579 × 108
MonotonicityNot monotonic
2023-01-25T09:43:02.118985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19911 151
 
0.4%
27427 114
 
0.3%
2187 106
 
0.3%
357 93
 
0.3%
49555 87
 
0.3%
18740 85
 
0.2%
18982 80
 
0.2%
30202 78
 
0.2%
23680 77
 
0.2%
14520 76
 
0.2%
Other values (4268) 33167
97.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
34 1
 
< 0.1%
107 2
 
< 0.1%
124 33
0.1%
175 10
 
< 0.1%
199 6
 
< 0.1%
209 7
 
< 0.1%
211 8
 
< 0.1%
236 4
 
< 0.1%
249 12
 
< 0.1%
ValueCountFrequency (%)
99995 2
 
< 0.1%
99982 17
< 0.1%
99944 4
 
< 0.1%
99938 7
< 0.1%
99901 6
 
< 0.1%
99897 1
 
< 0.1%
99893 1
 
< 0.1%
99883 3
 
< 0.1%
99881 2
 
< 0.1%
99873 1
 
< 0.1%

HADM_ID
Real number (ℝ)

Distinct7201
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150199.55
Minimum100018
Maximum199995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:02.257833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum100018
5-th percentile104896
Q1124577
median150542
Q3175905.5
95-th percentile195408.5
Maximum199995
Range99977
Interquartile range (IQR)51328.5

Descriptive statistics

Standard deviation29381.552
Coefficient of variation (CV)0.19561678
Kurtosis-1.2370096
Mean150199.55
Median Absolute Deviation (MAD)25837
Skewness-0.013547159
Sum5.1239073 × 109
Variance8.6327558 × 108
MonotonicityNot monotonic
2023-01-25T09:43:02.384925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171106 91
 
0.3%
111955 86
 
0.3%
175448 80
 
0.2%
117448 78
 
0.2%
111585 75
 
0.2%
124705 69
 
0.2%
108174 58
 
0.2%
150854 55
 
0.2%
148086 53
 
0.2%
133854 53
 
0.2%
Other values (7191) 33416
98.0%
ValueCountFrequency (%)
100018 3
 
< 0.1%
100020 1
 
< 0.1%
100035 10
< 0.1%
100036 2
 
< 0.1%
100039 7
< 0.1%
100045 6
< 0.1%
100061 1
 
< 0.1%
100068 4
 
< 0.1%
100091 1
 
< 0.1%
100099 1
 
< 0.1%
ValueCountFrequency (%)
199995 2
 
< 0.1%
199993 24
0.1%
199981 1
 
< 0.1%
199948 1
 
< 0.1%
199940 1
 
< 0.1%
199925 1
 
< 0.1%
199900 7
 
< 0.1%
199895 3
 
< 0.1%
199886 1
 
< 0.1%
199876 9
 
< 0.1%

DISEASE_DURATION
Real number (ℝ)

Distinct6248
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.317381
Minimum0.22986111
Maximum164.36597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:02.522449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.22986111
5-th percentile4.2520486
Q18.9319444
median15.134722
Q325.9875
95-th percentile52.042361
Maximum164.36597
Range164.13611
Interquartile range (IQR)17.055556

Descriptive statistics

Standard deviation16.999925
Coefficient of variation (CV)0.83671836
Kurtosis6.3218489
Mean20.317381
Median Absolute Deviation (MAD)7.5305556
Skewness2.1570897
Sum693107.12
Variance288.99747
MonotonicityNot monotonic
2023-01-25T09:43:02.638573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.65208333 91
 
0.3%
86.84791667 86
 
0.3%
105.5798611 80
 
0.2%
106.8319444 78
 
0.2%
78.72916667 75
 
0.2%
49.04652778 73
 
0.2%
91.60347222 69
 
0.2%
60.83263889 58
 
0.2%
42.54305556 56
 
0.2%
55.60347222 55
 
0.2%
Other values (6238) 33393
97.9%
ValueCountFrequency (%)
0.2298611111 1
< 0.1%
0.6423611111 1
< 0.1%
0.65625 1
< 0.1%
0.9284722222 1
< 0.1%
0.9979166667 1
< 0.1%
1.038888889 1
< 0.1%
1.040972222 1
< 0.1%
1.050694444 1
< 0.1%
1.056944444 2
< 0.1%
1.061111111 1
< 0.1%
ValueCountFrequency (%)
164.3659722 5
 
< 0.1%
106.8319444 78
0.2%
105.5798611 80
0.2%
99.81666667 3
 
< 0.1%
96.51041667 52
0.2%
93.94027778 26
 
0.1%
91.65208333 91
0.3%
91.60347222 69
0.2%
86.84791667 86
0.3%
85.42083333 49
0.1%

Height
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)2.2%
Missing31373
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean65.977614
Minimum0
Maximum95.9
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:02.773953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q163
median66
Q370
95-th percentile72
Maximum95.9
Range95.9
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6755045
Coefficient of variation (CV)0.08602167
Kurtosis35.78247
Mean65.977614
Median Absolute Deviation (MAD)3
Skewness-3.8443655
Sum180844.64
Variance32.211352
MonotonicityNot monotonic
2023-01-25T09:43:02.894377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 250
 
0.7%
66 237
 
0.7%
65 232
 
0.7%
68 219
 
0.6%
62 195
 
0.6%
64 193
 
0.6%
67 185
 
0.5%
72 181
 
0.5%
63 172
 
0.5%
69 164
 
0.5%
Other values (50) 713
 
2.1%
(Missing) 31373
92.0%
ValueCountFrequency (%)
0 2
< 0.1%
5 1
 
< 0.1%
5.5 1
 
< 0.1%
6.2 1
 
< 0.1%
24 2
< 0.1%
25 2
< 0.1%
26 1
 
< 0.1%
27 2
< 0.1%
28 3
< 0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
95.9 1
 
< 0.1%
83 1
 
< 0.1%
80 1
 
< 0.1%
79 2
 
< 0.1%
78.5 1
 
< 0.1%
78 7
 
< 0.1%
77 3
 
< 0.1%
76 8
 
< 0.1%
75 9
 
< 0.1%
74 45
0.1%

AGE
Real number (ℝ)

Distinct5327
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.567661
Minimum19.175342
Maximum89.060274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:03.025894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19.175342
5-th percentile46.315068
Q162.10137
median72.593151
Q380.969863
95-th percentile89
Maximum89.060274
Range69.884932
Interquartile range (IQR)18.868493

Descriptive statistics

Standard deviation13.272869
Coefficient of variation (CV)0.18808713
Kurtosis0.23166088
Mean70.567661
Median Absolute Deviation (MAD)9.3328767
Skewness-0.71044934
Sum2407345.2
Variance176.16905
MonotonicityNot monotonic
2023-01-25T09:43:03.161255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 2440
 
7.2%
66.39452055 91
 
0.3%
75.37260274 86
 
0.3%
75.88767123 81
 
0.2%
83.34246575 80
 
0.2%
75.28767123 78
 
0.2%
42.78630137 69
 
0.2%
85.10136986 67
 
0.2%
60.35616438 66
 
0.2%
74.53150685 59
 
0.2%
Other values (5317) 30997
90.9%
ValueCountFrequency (%)
19.17534247 4
< 0.1%
19.45205479 2
 
< 0.1%
20.50958904 1
 
< 0.1%
20.5260274 6
< 0.1%
20.80273973 2
 
< 0.1%
21.07123288 2
 
< 0.1%
21.27945205 8
< 0.1%
21.46849315 2
 
< 0.1%
21.51506849 1
 
< 0.1%
21.5260274 1
 
< 0.1%
ValueCountFrequency (%)
89.06027397 1
 
< 0.1%
89.03835616 4
 
< 0.1%
89.03561644 6
 
< 0.1%
89.02739726 5
 
< 0.1%
89.02191781 3
 
< 0.1%
89 2440
7.2%
88.99452055 5
 
< 0.1%
88.9890411 2
 
< 0.1%
88.97534247 1
 
< 0.1%
88.96712329 3
 
< 0.1%

GENDER_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
18428 
1
15686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

Length

2023-01-25T09:43:03.284534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:03.421450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

Most occurring characters

ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18428
54.0%
1 15686
46.0%

GENDER_M
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
1
18428 
0
15686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%

Length

2023-01-25T09:43:03.514379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:03.618005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%

Most occurring characters

ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18428
54.0%
0 15686
46.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
31465 
1
 
2649

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%

Length

2023-01-25T09:43:03.704826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:03.818377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31465
92.2%
1 2649
 
7.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
34081 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%

Length

2023-01-25T09:43:03.898432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:03.987939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34081
99.9%
1 33
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
17539 
1
16575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%

Length

2023-01-25T09:43:04.067549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:04.164908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%

Most occurring characters

ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17539
51.4%
1 16575
48.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
33700 
1
 
414

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%

Length

2023-01-25T09:43:04.247633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:04.338013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33700
98.8%
1 414
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
26845 
1
7269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%

Length

2023-01-25T09:43:04.420765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:04.521376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26845
78.7%
1 7269
 
21.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
33956 
1
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%

Length

2023-01-25T09:43:04.600977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:04.692361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33956
99.5%
1 158
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0
27697 
1
6417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34114
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

Length

2023-01-25T09:43:04.770483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:04.864998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 34114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27697
81.2%
1 6417
 
18.8%

REL_DAY
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct128
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2226652
Minimum0
Maximum377
Zeros7097
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:04.961936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile27
Maximum377
Range377
Interquartile range (IQR)8

Descriptive statistics

Standard deviation13.985055
Coefficient of variation (CV)1.9362735
Kurtosis294.94974
Mean7.2226652
Median Absolute Deviation (MAD)3
Skewness12.680053
Sum246394
Variance195.58176
MonotonicityNot monotonic
2023-01-25T09:43:05.086620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7097
20.8%
1 4869
14.3%
2 3457
10.1%
3 2627
 
7.7%
4 2015
 
5.9%
5 1640
 
4.8%
6 1358
 
4.0%
7 1171
 
3.4%
8 994
 
2.9%
9 868
 
2.5%
Other values (118) 8018
23.5%
ValueCountFrequency (%)
0 7097
20.8%
1 4869
14.3%
2 3457
10.1%
3 2627
 
7.7%
4 2015
 
5.9%
5 1640
 
4.8%
6 1358
 
4.0%
7 1171
 
3.4%
8 994
 
2.9%
9 868
 
2.5%
ValueCountFrequency (%)
377 1
< 0.1%
376 1
< 0.1%
375 1
< 0.1%
374 1
< 0.1%
373 1
< 0.1%
372 1
< 0.1%
371 1
< 0.1%
370 1
< 0.1%
369 1
< 0.1%
368 1
< 0.1%

Daily Weight
Real number (ℝ)

MISSING
SKEWED

Distinct2646
Distinct (%)21.3%
Missing21696
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean435.0131
Minimum0
Maximum4327454.1
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:05.216792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54
Q169.400002
median82.8
Q398.7
95-th percentile132.7
Maximum4327454.1
Range4327454.1
Interquartile range (IQR)29.299998

Descriptive statistics

Standard deviation38832.742
Coefficient of variation (CV)89.267983
Kurtosis12417.989
Mean435.0131
Median Absolute Deviation (MAD)14.4
Skewness111.43601
Sum5401992.6
Variance1.5079818 × 109
MonotonicityNot monotonic
2023-01-25T09:43:05.334020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 70
 
0.2%
80 62
 
0.2%
90 62
 
0.2%
70 62
 
0.2%
72 57
 
0.2%
79 56
 
0.2%
78 52
 
0.2%
85 51
 
0.1%
77 50
 
0.1%
82 49
 
0.1%
Other values (2636) 11847
34.7%
(Missing) 21696
63.6%
ValueCountFrequency (%)
0 12
< 0.1%
0.5 1
 
< 0.1%
3 2
 
< 0.1%
3.8 1
 
< 0.1%
8.4 1
 
< 0.1%
11.6 1
 
< 0.1%
12.88888889 1
 
< 0.1%
13.50555556 1
 
< 0.1%
26.20000076 1
 
< 0.1%
29.12 1
 
< 0.1%
ValueCountFrequency (%)
4327454.1 1
< 0.1%
511.5 1
< 0.1%
335 1
< 0.1%
257 1
< 0.1%
253 2
< 0.1%
245 1
< 0.1%
243.3 1
< 0.1%
240 2
< 0.1%
239.1 1
< 0.1%
237 1
< 0.1%

Heart Rate
Real number (ℝ)

Distinct13897
Distinct (%)40.8%
Missing81
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean85.513655
Minimum32.846154
Maximum338.70833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:05.962628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum32.846154
5-th percentile62.543939
Q174.68
median84.416667
Q395.32
95-th percentile111.64554
Maximum338.70833
Range305.86218
Interquartile range (IQR)20.64

Descriptive statistics

Standard deviation15.066489
Coefficient of variation (CV)0.17618811
Kurtosis2.4248073
Mean85.513655
Median Absolute Deviation (MAD)10.291667
Skewness0.49877151
Sum2910286.2
Variance226.9991
MonotonicityNot monotonic
2023-01-25T09:43:06.083015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 80
 
0.2%
80 77
 
0.2%
90 52
 
0.2%
82 47
 
0.1%
83 42
 
0.1%
81 42
 
0.1%
88 40
 
0.1%
84 37
 
0.1%
77 37
 
0.1%
91 36
 
0.1%
Other values (13887) 33543
98.3%
(Missing) 81
 
0.2%
ValueCountFrequency (%)
32.84615385 1
< 0.1%
34.94915254 1
< 0.1%
36.4137931 1
< 0.1%
36.95454545 1
< 0.1%
37.59259259 1
< 0.1%
37.8 1
< 0.1%
38.55172414 1
< 0.1%
39.14814815 1
< 0.1%
39.63636364 1
< 0.1%
39.91666667 1
< 0.1%
ValueCountFrequency (%)
338.7083333 1
< 0.1%
162.6956522 1
< 0.1%
161.4827586 1
< 0.1%
160.40625 1
< 0.1%
156.0769231 1
< 0.1%
155 1
< 0.1%
154.8636364 1
< 0.1%
154.2592593 1
< 0.1%
152.826087 1
< 0.1%
152.25 1
< 0.1%

Heart Rate Alarm - Low
Real number (ℝ)

MISSING
SKEWED

Distinct154
Distinct (%)0.8%
Missing13791
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean55.859995
Minimum15
Maximum16743.333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:06.209280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile50
Q150
median50
Q360
95-th percentile60
Maximum16743.333
Range16728.333
Interquartile range (IQR)10

Descriptive statistics

Standard deviation157.72149
Coefficient of variation (CV)2.8235142
Kurtosis8499.0925
Mean55.859995
Median Absolute Deviation (MAD)2.5
Skewness90.129095
Sum1135242.7
Variance24876.069
MonotonicityNot monotonic
2023-01-25T09:43:06.338385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 9679
28.4%
60 5629
16.5%
55 1910
 
5.6%
56.66666667 567
 
1.7%
53.33333333 522
 
1.5%
52.5 351
 
1.0%
57.5 324
 
0.9%
45 182
 
0.5%
47.5 96
 
0.3%
51.66666667 69
 
0.2%
Other values (144) 994
 
2.9%
(Missing) 13791
40.4%
ValueCountFrequency (%)
15 2
< 0.1%
16 1
 
< 0.1%
22 2
< 0.1%
25 1
 
< 0.1%
25.66666667 1
 
< 0.1%
27.33333333 1
 
< 0.1%
27.5 1
 
< 0.1%
28 3
< 0.1%
28.33333333 1
 
< 0.1%
28.8 1
 
< 0.1%
ValueCountFrequency (%)
16743.33333 1
 
< 0.1%
12567.5 1
 
< 0.1%
8395 1
 
< 0.1%
426 1
 
< 0.1%
300 1
 
< 0.1%
230 1
 
< 0.1%
220 1
 
< 0.1%
200 1
 
< 0.1%
140 1
 
< 0.1%
130 3
< 0.1%

Heart rate Alarm - High
Real number (ℝ)

MISSING
SKEWED

Distinct206
Distinct (%)1.0%
Missing13791
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean124.53386
Minimum1
Maximum40130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:06.470590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile110
Q1120
median120
Q3120
95-th percentile137.5
Maximum40130
Range40129
Interquartile range (IQR)0

Descriptive statistics

Standard deviation284.69609
Coefficient of variation (CV)2.2860939
Kurtosis19192.799
Mean124.53386
Median Absolute Deviation (MAD)0
Skewness136.86706
Sum2530901.6
Variance81051.866
MonotonicityNot monotonic
2023-01-25T09:43:06.596363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 14258
41.8%
130 998
 
2.9%
125 685
 
2.0%
110 500
 
1.5%
140 469
 
1.4%
135 299
 
0.9%
100 282
 
0.8%
115 230
 
0.7%
122.5 223
 
0.7%
123.3333333 190
 
0.6%
Other values (196) 2189
 
6.4%
(Missing) 13791
40.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
20 2
< 0.1%
45 1
 
< 0.1%
50 4
< 0.1%
55 2
< 0.1%
60 2
< 0.1%
65 1
 
< 0.1%
66 1
 
< 0.1%
66.66666667 1
 
< 0.1%
70 2
< 0.1%
ValueCountFrequency (%)
40130 1
< 0.1%
4110 2
< 0.1%
2407.142857 1
< 0.1%
2120 1
< 0.1%
1052.5 1
< 0.1%
820 1
< 0.1%
685 1
< 0.1%
675 1
< 0.1%
670 1
< 0.1%
665 1
< 0.1%

Oxygen_Saturation
Real number (ℝ)

Distinct4414
Distinct (%)13.0%
Missing118
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean105.1349
Minimum0
Maximum276753.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:06.731063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93.321071
Q195.76
median97.211324
Q398.541667
95-th percentile99.75
Maximum276753.91
Range276753.91
Interquartile range (IQR)2.7816667

Descriptive statistics

Standard deviation1500.4793
Coefficient of variation (CV)14.271942
Kurtosis33995.255
Mean105.1349
Median Absolute Deviation (MAD)1.3852368
Skewness184.37701
Sum3574166.2
Variance2251438
MonotonicityNot monotonic
2023-01-25T09:43:06.845106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 444
 
1.3%
97 297
 
0.9%
98 290
 
0.9%
96 255
 
0.7%
99 245
 
0.7%
95 188
 
0.6%
97.5 166
 
0.5%
96.5 158
 
0.5%
98.5 147
 
0.4%
99.5 136
 
0.4%
Other values (4404) 31670
92.8%
ValueCountFrequency (%)
0 1
< 0.1%
18 1
< 0.1%
50 1
< 0.1%
52.27777778 1
< 0.1%
61.36363636 1
< 0.1%
63 1
< 0.1%
65.1875 1
< 0.1%
66.11111111 1
< 0.1%
67.76190476 1
< 0.1%
68 1
< 0.1%
ValueCountFrequency (%)
276753.913 1
< 0.1%
503.6666667 1
< 0.1%
490.72 1
< 0.1%
461.5384615 1
< 0.1%
415.7 1
< 0.1%
401.4242424 1
< 0.1%
144.75 1
< 0.1%
136.4583333 1
< 0.1%
132.0869565 1
< 0.1%
130.96 1
< 0.1%

Oxygen_Saturation_Alarm_High
Real number (ℝ)

MISSING
SKEWED

Distinct148
Distinct (%)0.5%
Missing3042
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean104.85832
Minimum1
Maximum85100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:06.976341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum85100
Range85099
Interquartile range (IQR)0

Descriptive statistics

Standard deviation512.23926
Coefficient of variation (CV)4.885061
Kurtosis24518.742
Mean104.85832
Median Absolute Deviation (MAD)0
Skewness150.57645
Sum3258157.6
Variance262389.06
MonotonicityNot monotonic
2023-01-25T09:43:07.092020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 30521
89.5%
95 42
 
0.1%
10 41
 
0.1%
30 37
 
0.1%
130 31
 
0.1%
120 25
 
0.1%
90 22
 
0.1%
35 18
 
0.1%
110 18
 
0.1%
96.66666667 16
 
< 0.1%
Other values (138) 301
 
0.9%
(Missing) 3042
 
8.9%
ValueCountFrequency (%)
1 2
 
< 0.1%
8 1
 
< 0.1%
10 41
0.1%
19 1
 
< 0.1%
30 37
0.1%
30.38461538 1
 
< 0.1%
30.4 1
 
< 0.1%
30.77777778 1
 
< 0.1%
30.95238095 1
 
< 0.1%
34 1
 
< 0.1%
ValueCountFrequency (%)
85100 1
 
< 0.1%
20102 1
 
< 0.1%
16780.83333 1
 
< 0.1%
10085 2
< 0.1%
3428.333333 3
< 0.1%
2596.25 1
 
< 0.1%
2097 2
< 0.1%
1518.571429 1
 
< 0.1%
550 1
 
< 0.1%
400.3333333 1
 
< 0.1%

Oxygen_Saturation_Alarm_Low
Real number (ℝ)

MISSING
SKEWED

Distinct697
Distinct (%)2.2%
Missing3008
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean91.290439
Minimum2
Maximum30093.333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:07.217180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile89
Q190
median90
Q391.683158
95-th percentile93
Maximum30093.333
Range30091.333
Interquartile range (IQR)1.6831579

Descriptive statistics

Standard deviation170.24891
Coefficient of variation (CV)1.864915
Kurtosis31008.275
Mean91.290439
Median Absolute Deviation (MAD)0
Skewness175.95492
Sum2839680.4
Variance28984.691
MonotonicityNot monotonic
2023-01-25T09:43:07.333001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 18044
52.9%
92 5405
 
15.8%
93 1599
 
4.7%
91 1182
 
3.5%
88 500
 
1.5%
91.33333333 385
 
1.1%
89 380
 
1.1%
91.5 353
 
1.0%
90.66666667 335
 
1.0%
92.5 226
 
0.7%
Other values (687) 2697
 
7.9%
(Missing) 3008
 
8.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
8 67
0.2%
9 7
 
< 0.1%
10 2
 
< 0.1%
20.52631579 1
 
< 0.1%
20.92307692 1
 
< 0.1%
35.33333333 1
 
< 0.1%
41.4 1
 
< 0.1%
45 1
 
< 0.1%
48 1
 
< 0.1%
ValueCountFrequency (%)
30093.33333 1
 
< 0.1%
644 1
 
< 0.1%
508.5 1
 
< 0.1%
386.6666667 1
 
< 0.1%
368 1
 
< 0.1%
299.25 1
 
< 0.1%
292.5 1
 
< 0.1%
225 2
 
< 0.1%
113.3333333 1
 
< 0.1%
100 15
< 0.1%

Oxygen_Pressure
Real number (ℝ)

Distinct2614
Distinct (%)22.1%
Missing22285
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean124.64397
Minimum20
Maximum688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:07.459064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile65
Q187.333333
median109.5
Q3142.6
95-th percentile241.12571
Maximum688
Range668
Interquartile range (IQR)55.266667

Descriptive statistics

Standard deviation59.816339
Coefficient of variation (CV)0.47989756
Kurtosis10.513181
Mean124.64397
Median Absolute Deviation (MAD)25.5
Skewness2.4832388
Sum1474413.6
Variance3577.9944
MonotonicityNot monotonic
2023-01-25T09:43:07.582097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 95
 
0.3%
89 86
 
0.3%
84 84
 
0.2%
87 79
 
0.2%
88 77
 
0.2%
95 76
 
0.2%
85 75
 
0.2%
100 74
 
0.2%
98 74
 
0.2%
106 74
 
0.2%
Other values (2604) 11035
32.3%
(Missing) 22285
65.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
24 1
 
< 0.1%
25 2
< 0.1%
29 2
< 0.1%
30 1
 
< 0.1%
31 2
< 0.1%
32 3
< 0.1%
33 3
< 0.1%
ValueCountFrequency (%)
688 1
< 0.1%
675 1
< 0.1%
673 1
< 0.1%
672 1
< 0.1%
669 1
< 0.1%
662 1
< 0.1%
640 1
< 0.1%
634.3499756 1
< 0.1%
619 1
< 0.1%
545 1
< 0.1%

Blood_Pressure_Manual_Systolic
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct142
Distinct (%)50.2%
Missing33831
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean115.70039
Minimum48
Maximum201.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:07.714914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile80
Q198
median112
Q3132
95-th percentile164
Maximum201.5
Range153.5
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.045738
Coefficient of variation (CV)0.22511367
Kurtosis0.49552196
Mean115.70039
Median Absolute Deviation (MAD)16
Skewness0.65857325
Sum32743.209
Variance678.38048
MonotonicityNot monotonic
2023-01-25T09:43:07.835403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 10
 
< 0.1%
90 10
 
< 0.1%
110 8
 
< 0.1%
102 6
 
< 0.1%
124 6
 
< 0.1%
106 6
 
< 0.1%
112 5
 
< 0.1%
96 5
 
< 0.1%
88 5
 
< 0.1%
120 5
 
< 0.1%
Other values (132) 217
 
0.6%
(Missing) 33831
99.2%
ValueCountFrequency (%)
48 1
< 0.1%
68 1
< 0.1%
70 2
< 0.1%
70.5 1
< 0.1%
71 1
< 0.1%
72 1
< 0.1%
72.66666667 1
< 0.1%
74 1
< 0.1%
76 2
< 0.1%
76.5 1
< 0.1%
ValueCountFrequency (%)
201.5 1
< 0.1%
198 1
< 0.1%
195 1
< 0.1%
186 1
< 0.1%
183 2
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
176.5 1
< 0.1%
175 1
< 0.1%
170 1
< 0.1%

Blood_Pressure_Manual_Diastolic
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct108
Distinct (%)40.8%
Missing33849
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean62.170249
Minimum28.5
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:07.975045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28.5
5-th percentile41.68
Q152
median60
Q370
95-th percentile90.8
Maximum136
Range107.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.437021
Coefficient of variation (CV)0.24830238
Kurtosis2.9056956
Mean62.170249
Median Absolute Deviation (MAD)9
Skewness1.201205
Sum16475.116
Variance238.30161
MonotonicityNot monotonic
2023-01-25T09:43:08.099987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
0.1%
50 13
 
< 0.1%
58 9
 
< 0.1%
62 8
 
< 0.1%
68 8
 
< 0.1%
54 7
 
< 0.1%
70 7
 
< 0.1%
48 6
 
< 0.1%
40 5
 
< 0.1%
56 5
 
< 0.1%
Other values (98) 178
 
0.5%
(Missing) 33849
99.2%
ValueCountFrequency (%)
28.5 1
 
< 0.1%
30 1
 
< 0.1%
31.5 1
 
< 0.1%
35 2
 
< 0.1%
37.33333333 1
 
< 0.1%
39 1
 
< 0.1%
40 5
< 0.1%
41.5 1
 
< 0.1%
41.6 1
 
< 0.1%
42 4
< 0.1%
ValueCountFrequency (%)
136 1
< 0.1%
120 1
< 0.1%
114.5 1
< 0.1%
110 2
< 0.1%
109.6666667 1
< 0.1%
98 2
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
91.5 2
< 0.1%

Arterial_Blood_Pressure_Systolic
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct10076
Distinct (%)58.4%
Missing16857
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean118.36876
Minimum0
Maximum210.14286
Zeros18
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:08.228235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile92.82963
Q1105.45833
median115.92
Q3129.6
95-th percentile153.29661
Maximum210.14286
Range210.14286
Interquartile range (IQR)24.141667

Descriptive statistics

Standard deviation19.139368
Coefficient of variation (CV)0.16169274
Kurtosis2.062392
Mean118.36876
Median Absolute Deviation (MAD)11.656923
Skewness0.20249413
Sum2042689.6
Variance366.31542
MonotonicityNot monotonic
2023-01-25T09:43:08.352576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 27
 
0.1%
116 26
 
0.1%
114 26
 
0.1%
115 25
 
0.1%
108 24
 
0.1%
122 24
 
0.1%
118 24
 
0.1%
110 23
 
0.1%
111 23
 
0.1%
104 23
 
0.1%
Other values (10066) 17012
49.9%
(Missing) 16857
49.4%
ValueCountFrequency (%)
0 18
0.1%
4 1
 
< 0.1%
11 1
 
< 0.1%
11.2962963 1
 
< 0.1%
18 1
 
< 0.1%
24 1
 
< 0.1%
26.2 1
 
< 0.1%
30.5 1
 
< 0.1%
33 1
 
< 0.1%
40.5 1
 
< 0.1%
ValueCountFrequency (%)
210.1428571 1
< 0.1%
203.0357143 1
< 0.1%
200.64 1
< 0.1%
196.6666667 1
< 0.1%
192.25 1
< 0.1%
190.75 1
< 0.1%
190.7272727 1
< 0.1%
189.2352941 1
< 0.1%
189 1
< 0.1%
188.9090909 1
< 0.1%

Arterial_Blood_Pressure_Diastolic
Real number (ℝ)

HIGH CORRELATION
MISSING
SKEWED

Distinct8367
Distinct (%)48.5%
Missing16856
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean58.804376
Minimum0
Maximum2960.5721
Zeros20
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:08.475705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.541667
Q150.690934
median56.893021
Q364.2
95-th percentile77.25
Maximum2960.5721
Range2960.5721
Interquartile range (IQR)13.509066

Descriptive statistics

Standard deviation41.985169
Coefficient of variation (CV)0.71398036
Kurtosis3097.0481
Mean58.804376
Median Absolute Deviation (MAD)6.6930206
Skewness52.323336
Sum1014845.9
Variance1762.7544
MonotonicityNot monotonic
2023-01-25T09:43:08.594600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 56
 
0.2%
59 44
 
0.1%
49 41
 
0.1%
53 40
 
0.1%
51 39
 
0.1%
56 39
 
0.1%
61 38
 
0.1%
60 37
 
0.1%
54 37
 
0.1%
50 36
 
0.1%
Other values (8357) 16851
49.4%
(Missing) 16856
49.4%
ValueCountFrequency (%)
0 20
0.1%
4.962962963 1
 
< 0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
17.5 1
 
< 0.1%
18.85714286 1
 
< 0.1%
19.6 1
 
< 0.1%
20.22222222 1
 
< 0.1%
21.65714286 1
 
< 0.1%
23 1
 
< 0.1%
ValueCountFrequency (%)
2960.572143 1
< 0.1%
2566.074074 1
< 0.1%
2398.166667 1
< 0.1%
2070.966667 1
< 0.1%
1804.71875 1
< 0.1%
521.4117647 1
< 0.1%
429.1666667 1
< 0.1%
411.3333333 1
< 0.1%
358.9130435 1
< 0.1%
357.84 1
< 0.1%

Arterial_Blood_Pressure_Mean
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct8806
Distinct (%)50.8%
Missing16788
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean79.038385
Minimum-40
Maximum428.83333
Zeros5
Zeros (%)< 0.1%
Negative28
Negative (%)0.1%
Memory size266.6 KiB
2023-01-25T09:43:08.716823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-40
5-th percentile61.75
Q170
median76.458333
Q385.541667
95-th percentile102.76692
Maximum428.83333
Range468.83333
Interquartile range (IQR)15.541667

Descriptive statistics

Standard deviation17.822367
Coefficient of variation (CV)0.22549002
Kurtosis66.901707
Mean79.038385
Median Absolute Deviation (MAD)7.4175
Skewness4.8922111
Sum1369419.1
Variance317.63678
MonotonicityNot monotonic
2023-01-25T09:43:08.833085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 43
 
0.1%
74 43
 
0.1%
76 41
 
0.1%
78 38
 
0.1%
72 38
 
0.1%
73 36
 
0.1%
81 35
 
0.1%
70 34
 
0.1%
85 31
 
0.1%
71 31
 
0.1%
Other values (8796) 16956
49.7%
(Missing) 16788
49.2%
ValueCountFrequency (%)
-40 1
< 0.1%
-34.33333333 1
< 0.1%
-31.2 1
< 0.1%
-28.66666667 1
< 0.1%
-26 1
< 0.1%
-23.25 1
< 0.1%
-21 1
< 0.1%
-19 1
< 0.1%
-15.35 1
< 0.1%
-15.14285714 1
< 0.1%
ValueCountFrequency (%)
428.8333333 1
< 0.1%
414.2173913 1
< 0.1%
387.125 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
341.28 1
< 0.1%
338.5 1
< 0.1%
334.6666667 1
< 0.1%
332.2916667 1
< 0.1%
329.6 1
< 0.1%

GROUP_ID
Real number (ℝ)

Distinct7201
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1697403 × 1010
Minimum30104557
Maximum9.9995138 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size266.6 KiB
2023-01-25T09:43:08.957190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30104557
5-th percentile3.7221508 × 109
Q11.8208398 × 1010
median3.0678197 × 1010
Q36.6786111 × 1010
95-th percentile9.3870128 × 1010
Maximum9.9995138 × 1010
Range9.9965033 × 1010
Interquartile range (IQR)4.8577714 × 1010

Descriptive statistics

Standard deviation2.9057837 × 1010
Coefficient of variation (CV)0.69687401
Kurtosis-1.059683
Mean4.1697403 × 1010
Median Absolute Deviation (MAD)2.0182014 × 1010
Skewness0.46880992
Sum1.4224652 × 1015
Variance8.4435787 × 1020
MonotonicityNot monotonic
2023-01-25T09:43:09.086259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.991117111 × 101091
 
0.3%
4.955511196 × 101086
 
0.3%
2.742717545 × 101080
 
0.2%
3.020211745 × 101078
 
0.2%
3.278811158 × 101075
 
0.2%
1.45201247 × 101069
 
0.2%
7.369510817 × 101058
 
0.2%
2.131215085 × 101055
 
0.2%
2.381114809 × 101053
 
0.2%
8.178613385 × 101053
 
0.2%
Other values (7191) 33416
98.0%
ValueCountFrequency (%)
30104557 1
 
< 0.1%
34144319 1
 
< 0.1%
107174162 1
 
< 0.1%
107182383 1
 
< 0.1%
124112906 7
 
< 0.1%
124134369 1
 
< 0.1%
124138376 22
0.1%
124172461 3
 
< 0.1%
175159223 1
 
< 0.1%
175176764 9
< 0.1%
ValueCountFrequency (%)
9.999513781 × 10102
 
< 0.1%
9.998218379 × 10106
< 0.1%
9.998215145 × 10107
< 0.1%
9.998211275 × 10104
< 0.1%
9.994418565 × 10104
< 0.1%
9.993810382 × 10107
< 0.1%
9.990113171 × 10106
< 0.1%
9.989716291 × 10101
 
< 0.1%
9.989312835 × 10101
 
< 0.1%
9.988319852 × 10101
 
< 0.1%

DOD_LABEL
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.6 KiB
0.0
28999 
1.0
5115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters102342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28999
85.0%
1.0 5115
 
15.0%

Length

2023-01-25T09:43:09.202022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T09:43:09.293811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28999
85.0%
1.0 5115
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 63113
61.7%
. 34114
33.3%
1 5115
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68228
66.7%
Other Punctuation 34114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63113
92.5%
1 5115
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 34114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63113
61.7%
. 34114
33.3%
1 5115
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63113
61.7%
. 34114
33.3%
1 5115
 
5.0%

Interactions

2023-01-25T09:42:57.981562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.019365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.448214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.663903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.501403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.959498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.547416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.787210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.872751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.232418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.758983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.015909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.219248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.782262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.097653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.093188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.702276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.584092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.144050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.334114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.094002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.272351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.549449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.777150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.694349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.071901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.657797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.885595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.002993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.386607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.870202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.125647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.384403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.896293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.202784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.271588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.797947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.686184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.250598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.437472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.200271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.365795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.673311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.894388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.849605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.207681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.785255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.991064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.123997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.764742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.984022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.240525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.494130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.007470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.314070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.437509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.894287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.787493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.362518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.639917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.302392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.457642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.766524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:17.033576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.030008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.327071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.887999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.088766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.241211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.888326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.095565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.341692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.600110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.114263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.415861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.630397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.997003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.885040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.465025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.788199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.409151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.565874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.873745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:17.152298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.191967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.456246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.000736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.191067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.363252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.994091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.205070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.444719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.711831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.229402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.519302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.769085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.080244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.002656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.574441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.923784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.520471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.811003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.008081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:17.294499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.367138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.573193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.122208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.304793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.491597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.106124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.323121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.558592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.833882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.348244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.634573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:46.920290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.177483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.109442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.698630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.039283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.626949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:12.926222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.132166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:17.422744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.486030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.681244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.230494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.412835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.604565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.216720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.432737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.664030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.938491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.460171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.742840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.059310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.268464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.212812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.810683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.142082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.741073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.019239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.244802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:17.776475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.583288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.791012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.339645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.510280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.728816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.322160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.537490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.781442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.042263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.569895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:43.848066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.212050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.360312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.332278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.915407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.277360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.846090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.130533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.381771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.079231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.686686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:22.902409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.452519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.618130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.844199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.441417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.654127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:36.890987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.150736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.680896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.034402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.323762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.446524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.435036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.024323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.475364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:58.958282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.245113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.487439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.212964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.792067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.019556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.567536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.727653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:29.972493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.561286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.770905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.006129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.261249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.799483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.240763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.467125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.542084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.552670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.135527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.595891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.077943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.353244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.608885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.353293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:20.915133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.151984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.696037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.822488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.134379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.681310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:34.893176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.125700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.380741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:41.924078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.387030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.597114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.632434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.672541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.256488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.745703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.183970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.460028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.700915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.464701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.013123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.264056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.807947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:27.925258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.258333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.789349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.014437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.236960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.489102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.035925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.569671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.714032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.730729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.776319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.363894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.865879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.291788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.565328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.807481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.570552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.126382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.371783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:25.914746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.025596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.376289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:32.894929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.126020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.341153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.596480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.144526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.799166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.847521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.829933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:51.887538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.468804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:56.966639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.407617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.670960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:15.914109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.679014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.230813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.531791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.033115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.124126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.492400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.006335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.239386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.451728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.699131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.254085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:44.956137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:47.986027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:49.934482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.401714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.579885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.154350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.514113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.767206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.013749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.788568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.334483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.667131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.139792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.237474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.599012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.114103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.343042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.560792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.804646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.371261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.139338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.089029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.030197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.525881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.692807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.307979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.612667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:13.897598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.093219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:18.891634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.438374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.775949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.238483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.335509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.696025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.217039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.447680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.659005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:39.912755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.538696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.323824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.193068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.126361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.624128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.792179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.454508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.712144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.009134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.194242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.007676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.526243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.879179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.342770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.429656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.794515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.311285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.542076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.756407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.019643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.659523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.474641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.287449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.218495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.724502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:54.885338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.563796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.824203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.125286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.304168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.113180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.641271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:23.986248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.449942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.546542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:30.899763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.423288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.664447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.857968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.129110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.765248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.631653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.392417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.314979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.827015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.003037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.662226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:59.972002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.233260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.421380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.222514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.740597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.104658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.560629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.657051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.011541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.535341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.780922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:37.969127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.239203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.878743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.784943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.491199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.400888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:52.932389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.115371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.773997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:43:00.082931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:14.337814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:16.539609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:19.341575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:21.844228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:24.426238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:26.682514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:28.762511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:31.125411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:33.641218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:35.895958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:38.092202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:40.659216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:42.992085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:45.934446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:48.587913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:50.490564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:53.040099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:55.223081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-25T09:42:57.874560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-25T09:43:09.405027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-25T09:43:09.766561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-25T09:43:10.126871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-25T09:43:10.488918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-25T09:43:10.824620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-25T09:43:11.012540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-25T09:43:00.266379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-25T09:43:01.024016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-25T09:43:01.546412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SUBJECT_IDHADM_IDDISEASE_DURATIONHeightAGEGENDER_FGENDER_MMARITAL_STATUS_DIVORCEDMARITAL_STATUS_LIFE PARTNERMARITAL_STATUS_MARRIEDMARITAL_STATUS_SEPARATEDMARITAL_STATUS_SINGLEMARITAL_STATUS_UNKNOWN (DEFAULT)MARITAL_STATUS_WIDOWEDREL_DAYDaily WeightHeart RateHeart Rate Alarm - LowHeart rate Alarm - HighOxygen_SaturationOxygen_Saturation_Alarm_HighOxygen_Saturation_Alarm_LowOxygen_PressureBlood_Pressure_Manual_SystolicBlood_Pressure_Manual_DiastolicArterial_Blood_Pressure_SystolicArterial_Blood_Pressure_DiastolicArterial_Blood_Pressure_MeanGROUP_IDDOD_LABEL
0301045575.013889NaN89.0000000100100000NaN80.750000NaNNaN92.526316NaNNaNNaNNaNNaNNaNNaNNaN301045570.0
1341443192.62291765.089.0000000100100000NaN54.66666740.0120.096.160000100.090.0NaNNaNNaNNaNNaNNaN341443190.0
235717448628.282639NaN62.753425010010000097.50000064.428571NaNNaN99.166667100.090.0NaNNaNNaN127.42857169.28571493.7000003571744860.0
335717448628.282639NaN62.7534250100100001NaN74.580645NaNNaN98.212121100.090.0NaNNaNNaN126.29032364.61290385.7096773571744860.0
435717448628.282639NaN62.7534250100100002NaN83.590909NaNNaN95.807692100.091.0NaNNaNNaN125.81818265.63636486.6363643571744860.0
535717448628.282639NaN62.7534250100100003NaN74.923077NaNNaN97.185185100.092.0NaNNaNNaN113.19230857.46153877.3461543571744860.0
635717448628.282639NaN62.7534250100100004NaN76.360000NaNNaN95.333333100.092.0NaNNaNNaN139.91304363.58333388.3750003571744860.0
735717448628.282639NaN62.7534250100100005102.80000376.720000NaNNaN96.666667100.090.0NaNNaNNaN114.00000056.20000074.3913043571744860.0
835717448628.282639NaN62.7534250100100006NaN76.250000NaNNaN99.142857100.090.0NaNNaNNaN135.64285766.75000089.5000003571744860.0
935717448628.282639NaN62.7534250100100007103.09999871.000000NaNNaN97.200000100.092.2NaNNaNNaN118.77272753.63636474.4545453571744860.0
SUBJECT_IDHADM_IDDISEASE_DURATIONHeightAGEGENDER_FGENDER_MMARITAL_STATUS_DIVORCEDMARITAL_STATUS_LIFE PARTNERMARITAL_STATUS_MARRIEDMARITAL_STATUS_SEPARATEDMARITAL_STATUS_SINGLEMARITAL_STATUS_UNKNOWN (DEFAULT)MARITAL_STATUS_WIDOWEDREL_DAYDaily WeightHeart RateHeart Rate Alarm - LowHeart rate Alarm - HighOxygen_SaturationOxygen_Saturation_Alarm_HighOxygen_Saturation_Alarm_LowOxygen_PressureBlood_Pressure_Manual_SystolicBlood_Pressure_Manual_DiastolicArterial_Blood_Pressure_SystolicArterial_Blood_Pressure_DiastolicArterial_Blood_Pressure_MeanGROUP_IDDOD_LABEL
341049872418675411.051389NaN84.4684931000100001NaN112.33333360.000000125.093.291667100.089.0194.5NaNNaN121.64705945.88235371.176471987241867540.0
341059875318576419.44027864.077.8328771000100000NaN92.21621650.000000140.094.636364100.090.062.0NaNNaNNaNNaNNaN987531857641.0
341069875318576419.440278NaN77.832877100010000150.495.66666760.000000120.095.708333100.090.0NaNNaNNaNNaNNaNNaN987531857641.0
341079875318576419.440278NaN77.8328771000100002NaN95.78260960.000000120.092.086957100.089.0NaNNaNNaNNaNNaNNaN987531857641.0
341089875318576419.440278NaN77.832877100010000344.689.04347860.000000120.092.875000100.089.0NaNNaNNaNNaNNaNNaN987531857641.0
341099875318576419.440278NaN77.8328771000100004NaN85.833333NaNNaN93.500000NaNNaNNaNNaNNaNNaNNaNNaN987531857641.0
341109875318576419.440278NaN77.8328771000100005NaN88.06666750.000000120.095.066667100.090.064.0NaNNaNNaNNaNNaN987531857641.0
341119875318576419.440278NaN77.8328771000100006NaN78.95833360.000000120.097.541667100.090.0NaNNaNNaNNaNNaNNaN987531857641.0
341129875318576419.440278NaN77.832877100010000742.470.08333360.000000120.098.695652100.090.0NaNNaNNaNNaNNaNNaN987531857641.0
34113987551841561.18541762.080.1808221000000010NaN91.32000056.666667120.090.071429100.085.070.4NaNNaNNaNNaNNaN987551841560.0